activity
20192021
most citedContextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search

61 citations · 115 across the 8 of their papers we have counts for

collaborators

12 papers

cs.CV202161 cited

Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search

Chenyang Gao, Guanyu Cai, Xinyang Jiang +6

Text-based person search aims at retrieving target person in an image gallery using a descriptive sentence of that person. It is very challenging since modal gap makes effectively…

cs.CV20202 cited

One for More: Selecting Generalizable Samples for Generalizable ReID Model

Enwei Zhang, Xinyang Jiang, Hao Cheng +7

Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the p…

cs.CV2020

Pruning Filter in Filter

Fanxu Meng, Hao Cheng, Ke Li +4

Pruning has become a very powerful and effective technique to compress and accelerate modern neural networks. Existing pruning methods can be grouped into two categories: filter pr…

cs.CV2020

Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion

Jinpeng Wang, Yuting Gao, Ke Li +5

One significant factor we expect the video representation learning to capture, especially in contrast with the image representation learning, is the object motion. However, we foun…

cs.CV2020

Devil's in the Details: Aligning Visual Clues for Conditional Embedding in Person Re-Identification

Fufu Yu, Xinyang Jiang, Yifei Gong +5

Although Person Re-Identification has made impressive progress, difficult cases like occlusion, change of view-pointand similar clothing still bring great challenges. Besides overa…

cs.CV20208 cited

Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians

Shizhen Zhao, Changxin Gao, Jun Zhang +7

In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual. However, in a crowded scene, off-shelf-detectors may generate bou…